Machine Learning, Robotics,
During his Ph.D. Michael works on a joint project with ABB Corporate Research on bringing robot learning to industrial applications. The current assembly automation using robotics is limited by the high cost and long duration of robot programming and the inflexible programs. Hence, robotic automation is only applicable to standardised and high volume manufacturing. However, the trend for highly personalised products requires assembly automation for smaller volumes. With other words, future robots must perform thousand tasks several times rather than a single task thousand times. Robot learning promises the generation of intelligent robots capable of being programmed by workers rather than engineers and capable of transferring knowledge between tasks. Therefore, reducing cost for programming and time-to-deployment, which is a key enabler for robotic automation for low-volume and personalised manufacturing. Within this context, Michael first evaluates the current state of the art of robot learning by demonstrating the current capabilities on a small assembly task. Afterwards, he will focus his research on transferring knowledge between similar domain tasks.
Deep Learning, Non-Convex Optimisation, Reinforcement Learning, Structured Learning, Inductive Biases, Safe Exploration, Neuro-Inspired Learning, Neuromorphic Hardware
High-Speed Robotics, Industrial Manipulators, Humanoids, Dexterous Manipulation, Learning for Control, Optimal Control, Robust Control, Motion Representation
- , International Conference on Learning Representations (ICLR).
- , Robotics: Science and Systems (RSS).
- , International Conference on Machine Learning (ICML).
- , International Conference on Intelligent Robots and Systems (IROS).